• Communications for Statistical Applications and Methods
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• v.18 no.1
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• pp.147-153
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• 2011

In this paper a new sampling procedure for estimating the population mean is introduced. The performance of the new population mean estimator is discussed, along with its properties, and it is shown that the proposed method generates an unbiased estimator. The relative efficiency of the suggested estimator is computed, in regards to the simple random sample(SRS), and comparisons are made to the ranked set sampling(RSS) and extreme ranked set sampling(ERSS) estimators used for asymmetric distributions. The results indicate that the proposed estimator is more efficient than the estimators based on the ERSS. In addition, the folded ranked set sampling(FRSS) procedure has an advantage over the RSS and ERSS in that it reduces the number of unused sampling units.

• Communications for Statistical Applications and Methods
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• v.25 no.3
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• pp.283-296
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• 2018

Ranked set sampling, as introduced by McIntyre (Australian Journal of Agriculture Research, 3, 385-390, 1952), dealt with the estimation of the mean of one population. To deal with two or more variables, different forms of bivariate and multivariate ranked set sampling were suggested. For a technique to be useful, it should be easy to implement in practice. Bivariate ranked set sampling, as introduced by Al-Saleh and Zheng (Australian & New Zealand Journal of Statistics, 44, 221-232, 2002), is not easy to implement in practice, because it requires the judgment ranking of each of the combination of the order statistics of the two characteristics. This paper investigates two modifications that make the method easier to use. The first modification is based on ranking one variable and noting the rank of the other variable for one cycle, and do the reverse for another cycle. The second approach is based on ranking of one variable and giving the second variable the same rank (Concomitant Order Statistic) for one cycle and do the reverse for the other cycle. The two procedures are investigated for an estimation of the means of some well-known distributions. It is show that the suggested approaches can be used in practice and can be more efficient than using SRS. A real data set is used to illustrate the procedure.

• Communications for Statistical Applications and Methods
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• v.14 no.3
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• pp.641-648
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• 2007

We propose the new sampling method, called modified ranked ordering set sampling (MROSS). Kim and Kim (2003) suggested the sign test using the ranked ordering set sampling (ROSS), and showed that the asymptotic relative efficiency (ARE) of ROSS against RSS for sign test increases as sample size does. We propose the estimator for the population mean using MROSS. The relative precision (RP) of estimator of the population mean using MROSS method with respect to the usual estimator using modified RSS is higher, and when the underlying distribution is skewed, the bias of the proposed estimator is smaller than that of several ranked set sampling estimators.

• Communications for Statistical Applications and Methods
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• v.23 no.2
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• pp.119-129
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• 2016

A new sampling method is introduced based on the idea of a ranked set sampling scheme in which taken samples in each set are dependent on previous ones. Some theoretical results are presented and distribution-free confidence intervals are derived for the quantiles of any continuous population. It is shown numerically that the proposed sampling scheme may lead to 95% confidence intervals (especially for extreme quantiles) that cannot be found based on the ordinary ranked set sampling scheme presented by Chen (2000) and Balakrishnan and Li (2006). Optimality aspects of this scheme are investigated for both coverage probability and minimum expected length criteria. A real data set is also used to illustrate the proposed procedure. Conclusions are eventually stated.

• Communications for Statistical Applications and Methods
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• v.13 no.2
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• pp.419-428
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• 2006

The method of Reverse Ranked Ordering-Set Sampling(RROSS) as an opposed Ranked Ordering-Set Sampling(ROSS) and Ranked-Set Sampling(RSS) is discussed. We propose the test statistic using sign test on RROSS. This method is effective when observations are expensive and measurement is perhaps destructive or invasive. This method obtains more informations than ROSS and RSS. The asymptotic relative efficiencies of RROSS with respect to ROSS and RSS are always greater than 1 for all sample sizes. We consider a simple model to describe the effect of imperfect judgment errors.

• Journal of the Korean Statistical Society
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• v.34 no.2
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• pp.125-140
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• 2005

In this paper, we consider the estimation of the correlation coefficient in the bivariate normal distribution, based on a sample obtained using a modification of the moving extreme ranked set sampling technique (MERSS) that was introduced by Al-Saleh and Al-Hadhrami (2003a). The modification involves using a concomitant random variable. Nonparametric-type methods as well as the maximum likelihood estimation are considered under different settings. The obtained estimators are compared to their counterparts that are obtained based simple random sampling (SRS). It appears that the suggested estimators are more efficient

• Communications for Statistical Applications and Methods
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• v.25 no.1
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• pp.1-13
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• 2018

In many studies, a researcher attempts to describe a population where units are measured for multiple outcomes, or responses. In this paper, we present an efficient procedure based on ranked set sampling to estimate and perform hypothesis testing on a multivariate mean. The method is based on ranking on an auxiliary covariate, which is assumed to be correlated with the multivariate response, in order to improve the efficiency of the estimation. We showed that the proposed estimators developed under this sampling scheme are unbiased, have smaller variance in the multivariate sense, and are asymptotically Gaussian. We also demonstrated that the efficiency of multivariate regression estimator can be improved by using Ranked set sampling. A bootstrap routine is developed in the statistical software R to perform inference when the sample size is small. We use a simulation study to investigate the performance of the method under known conditions and apply the method to the biomarker data collected in China Health and Nutrition Survey (CHNS 2009) data.

• Communications for Statistical Applications and Methods
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• v.28 no.6
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• pp.643-653
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• 2021

Few studies are found in literature on estimation of population quantiles using the method of ranked set sampling (RSS). The optimal RSS strategy is to select observations with at most two fixed rank order statistics from different ranked sets. In this paper, a near optimal unbalanced RSS model for estimating p^th(0 < p < 1) population quantile is proposed. Main advantage of this model is to use each rank order statistics and is distributionfree. The asymptotic relative efficiency (ARE) for balanced RSS, unbalanced optimal and proposed near-optimal methods are computed for different values of p. We also compared these AREs with respect to simple random sampling. The results show that proposed unbalanced RSS performs uniformly better than balanced RSS for all set sizes and is very close to the optimal RSS for large set sizes. For the practical utility, the near optimal unbalanced RSS is recommended for estimating the quantiles.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.535-543
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• 2003

A ranked set sample version of the sign test is proposed for testing hypotheses concerning the quantiles of a population characteristic by Kaur, et. al(2002). In this paper, we proposed the ranked set sample Wilcoxon signed rank test for quantiles under equal allocation. We obtain the asymptotic property and the asymptotic relative efficiencies of the proposed test statistic with respect to Wilcoxon signed rank test of simple random sample for quantiles under equal allocation. We calculate the ARE of test statistics, the proposed test statistic is more efficient than simple random sampling for all quantiles. The relative advantage of ranked set sampling is greatest at the median and tapers off in the tails.

• Communications for Statistical Applications and Methods
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• v.15 no.2
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• pp.147-159
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• 2008

We consider using ranked set sampling methods to draw inference about the three well-known measures of overlap, namely Matusita's measure $\rho$, Morisita's measure $\lambda$ and Weitzman's measure $\Delta$. Two exponential populations with different means are considered. Due to the difficulties of calculating the precision or the bias of the resulting estimators of overlap measures, because there are no closed-form exact formulas for their variances and their exact sampling distributions, Monte Carlo evaluations are used. Confidence intervals for those measures are also constructed via the bootstrap method and Taylor series approximation.